Top AI and Data Science Tools and Techniques for 2022 and Beyond

 
By Jess Nguyen, NVIDIA

Want to know how artificial intelligence will change business in the next decade? World-class Kaggle Grandmasters, top data professionals, and influencers across several industries are gathering to inspire you with the most exciting data science advances. At NVIDIA GTC, they'll show you how to build and optimize AI and data science solutions that are faster, more efficient, and yield better results than previous solutions.

Every year, new data science techniques and tools emerge. Some practitioners build their ideas upon published papers. Others try their hand at entirely different methods based on what they learned during their thesis work or even Kaggle competitions.

However, the challenge can be daunting when experts attempt to implement these advanced capabilities on real-world datasets or within products. The abstract representation of the model and the lack of concrete examples make it difficult to improve your data science and machine learning pipelines.

At NVIDIA GTC, a free, virtual event taking place March 21–24, data scientists will share their top tools and techniques to optimize data pipelines. Some methods are library-specific, and others are framework-specific. At a glance, here’s what you can gain at GTC to put yourself ahead of the competition this year.

Learn How to Create Features from Tabular Data and Accelerate your Data Science Pipeline

 
Tabular data structures limit models' capabilities to learn relationships between features, but hand-crafted features can considerably boost model performance. Using knowledge gained from Kaggle and RecSys competitions, the NVIDIA team will introduce you to feature engineering techniques specific to tabular data. You'll learn how to accelerate your data science pipeline from data preprocessing and engineering to machine learning on GPUs, using RAPIDS open-source software.

Applying Lessons from Kaggle-winning Solutions to Real-world Problems

 
How could a chemical formula prediction model be converted for grocery delivery route planning? Could NFL helmet detection models help video conference background removal? Which time series competitions are the most useful when building a sales forecasting model? Join this live GTC panel discussion to hear how the Kaggle Grandmasters of NVIDIA (KGMON) team reuses its competition-winning models and techniques for solving real-world problems.

Accelerate ML and DL Recommender Systems on CPU and GPUs With NVIDIA Merlin

 
From distributed embeddings to GPU embedding caches, NVIDIA Merlin can fast-track your recommendation solution from ideation to production. This GTC session will show you how to develop faster solutions that leverage both CPU and GPU architectures. Examine our latest library of Merlin models and identify strategies for evaluating different recommender system models for your use case.

Building AI-based Recommender System Leveraging the Power of Deep Learning and GPU

 
AI-based recommender systems are a wonderful and cost-effective way to improve the shopping experience. This session examines the challenges unique to retail and reveals the best methods for delivering personalized experiences to differentiate you from competitors. Experts will demonstrate how to train and deploy real-time deep-learning models to enable personalized recommendations at scale.

Conversational AI Demystified

 
It's easier than ever to develop AI speech applications like virtual assistants and real-time transcription. Advanced tools and technologies make it easy to fine-tune and build scalable, responsive applications. This session will show you how to develop and deploy your first end-to-end conversational AI pipeline using NVIDIA Riva as an example.

There’s no doubt that AI and data science will continue to play an increasingly important role across each industry. To learn how you can apply these groundbreaking techniques to your own business or projects, be sure to register for GTC.